My graduate school training was as a structural biologist with a concentration in molecular and cellular biophysics. My graduate education and training at UNC Chapel Hill was in protein expression and purification, protein characterization, molecular biology, cellular biology, x-ray crystallography and protein structure determination. In addition, I gained experience using multiple bioinformatic tools, molecular genetics, mammalian cellular assays, and biophysical/biochemical protein characterization techniques.This multidisciplinary approach was further developed at Collaborations Pharmaceuticals, Inc. Currently, my experience at this company has focused on applying machine learning tools developed in-house, project management, assay development and data analysis with example publications shown below. The sheer diversity of these projects has given me expertise in numerous aspects of toxicology as well as with a broad range of human pathogens, including but not limited to Ebola, HIV and Tuberculosis. This expertise includes not only the mechanism of the pathogenicity of these diseases as well as general toxicology, but also extends to the biological and biophysical experiments needed to determine these. Having this knowledge has strongly influenced my ability to understand assay compatibility, which have proven to be integral in the developing accurate SAR models. Additional projects with peer-reviewed publications have focused on the absorption, distribution, metabolism, excretion (ADME), and pharmacokinetic (PK) assessment of novel pharmaceutics. My role as an associate director at Collaborations Pharmaceuticals has also been focused on project management and in the development of our proprietary software. I have contributed to over 40 published manuscripts along with multiple presentations at national conferences, with many focusing on machine learning modelling often with the addition of algorithm comparison. Our 2021 publication “Bioactivity Comparison across Multiple Machine Learning Algorithms Using over 5000 Datasets for Drug Discovery” was, as far as we are aware, the largest algorithm comparison to date. In 2023, several recent publications have focused on the discovery and characterization of antivirals, including but not limited to Hepatitis B, HIV and enterovirus D68 (EVD-68).